class_docstr: L1 Loss on MelSpectrogram.
  
  Args:
      sample_rate (int): Sample rate.
      n_fft (int): Number of fft.
      hop_length (int): Hop size.
      win_length (int): Window length.
      n_mels (int): Number of mel bins.
      f_min (float or None): Minimum frequency.
      f_max (float or None): Maximum frequency.
      log (bool): Whether to scale with log.
      normalized (bool): Whether to normalize the melspectrogram.
      floor_level (float): Floor level value based on human perception (default=1e-5).
class_docstr_tok: ["L1","Loss","on","MelSpectrogram",".","Args",":","sample_rate","(","int",")",":","Sample","rate",".","n_fft","(","int",")",":","Number","of","fft",".","hop_length","(","int",")",":","Hop","size",".","win_length","(","int",")",":","Window","length",".","n_mels","(","int",")",":","Number","of","mel","bins",".","f_min","(","float","or","None",")",":","Minimum","frequency",".","f_max","(","float","or","None",")",":","Maximum","frequency",".","log","(","bool",")",":","Whether","to","scale","with","log",".","normalized","(","bool",")",":","Whether","to","normalize","the","melspectrogram",".","floor_level","(","float",")",":","Floor","level","value","based","on","human","perception","(","default=1e-5",")","."]
code: def forward(self, x, y):
      self.melspec.to(x.device)
      s_x = self.melspec(x)
      s_y = self.melspec(y)
      return self.l1(s_x, s_y)
  
file_path: audiocraft/audiocraft/losses/specloss.py
imports: import typing
  import numpy
  from torchaudio.transforms import MelSpectrogram
  import torch
  from torch import nn
  from torch.nn import functional
  from ..modules import pad_for_conv1d
label_desc: Use image node_id 2 for calling the MelSpectrogramL1Loss obj's underlying member method code with example usage: obj.forward(x, y) and returns: self